Companies in the oil and gas industry need the ability to make accurate and timely decisions that depend heavily on good quality and reliable data. Data quality is synonymous with information quality, since poor data quality results in inaccurate information and poor business performance. For data to be qualified as being of good quality within the oil and gas industry, the data should possess some key characteristics such as accuracy, completeness, consistency, and timeliness.
Data accuracy measures the degree to which data correctly reflects the realities of conditions modeled. The oil and gas industry is highly reliant on accurate data because of the high risks involved. For example, planning a well using inaccurate information pressure data could result in serious complications, e.g., formation fracture or borehole instability, which could lead to cause significant damage to equipment while drilling the well.
Data completeness provides an indication of whether or not all the data necessary to meet the current and future business information demand are available in the data resource. One approach to measure completeness is to ensure that certain attributes always have assigned values. For example, a complete list may be required of all marker top and fluid fill predictions for all zones to be penetrated (or avoided).
Data consistency summarizes the validity, accuracy, usability and integrity of related data between applications and across a business. Quality data should be consistent across the board, such that data set values or naming conventions are uniform wherever such data sets occur. The consistency of data effectively ensures that data values in one data set are consistent with values in another data set. The expectation is that similar data values drawn from separate data sets should not conflict with each other.
Concurrency or timeliness refers to the degree to which information is current with the environment that the data models. This measures how up to date the data is, as well as its correctness in the face of possible time-related changes, which is particularly important in the oil and gas industry. An example of this is where Integrated Production Simulation Models (IPSM) are run using old or stale field production forecasts that do not reflect the changes to the reservoir since the forecast was first made, especially where current production data is available.
Data quality-related problems can cost oil and gas companies millions of dollars annually. Revenue opportunities are lost as a result of the inability to make strategic business decisions in a timely manner. In an attempt to solve data quality-related problems and achieve the key characteristics above, many companies employ data cleansing methodologies, resulting in short-term and costly improvements that have little effect in the long run, because the cleansing methodologies do not address the root causes of data defects.